For python 3.xx (Python 3.7.2)&

In [2]: pd.__version__ Out[2]: '0.23.4':

Options and Settings

For visualization of the dataframe pandas.set_option

import pandas as pd #import pandas package# for visualisation fo the float data once we read the float data:pd.set_option('display.html.table_schema', True) # to can see the dataframe/table as a htmlpd.set_option('display.precision', 5) # setting up the precision point so can see the data how looks, here is 5df = pd.DataFrame(np.random.randn(20,4)* 10 ** -12) # create random dataframe

Output of the data:

df.dtypes # check datatype for columns[output]:0    float641    float642    float643    float64dtype: object

Dataframe:

df # output of the dataframe[output]:0   1   2   30   -2.01082e-12    1.25911e-12 1.05556e-12 -5.68623e-131   -6.87126e-13    1.91950e-12 5.25925e-13 3.72696e-132   -1.48068e-12    6.34885e-14 -1.72694e-12    1.72906e-123   -5.78192e-14    2.08755e-13 6.80525e-13 1.49018e-124   -9.52408e-13    1.61118e-13 2.09459e-13 2.10940e-135   -2.30242e-13    -1.41352e-13    2.32575e-12 -5.08936e-136   1.16233e-12 6.17744e-13 1.63237e-12 1.59142e-127   1.76679e-13 -1.65943e-12    2.18727e-12 -8.45242e-138   7.66469e-13 1.29017e-13 -1.61229e-13    -3.00188e-139   9.61518e-13 9.71320e-13 8.36845e-14 -6.46556e-1310  -6.28390e-13    -1.17645e-12    -3.59564e-13    8.68497e-1311  3.12497e-13 2.00065e-13 -1.10691e-12    -2.94455e-1212  -1.08365e-14    5.36770e-13 1.60003e-12 9.19737e-1313  -1.85586e-13    1.27034e-12 -1.04802e-12    -3.08296e-1214  1.67438e-12 7.40403e-14 3.28035e-13 5.64615e-1415  -5.31804e-13    -6.68421e-13    2.68096e-13 8.37085e-1316  -6.25984e-13    1.81094e-13 -2.68336e-13    1.15757e-1217  7.38247e-13 -1.76528e-12    -4.72171e-13    -3.04658e-1318  -1.06099e-12    -1.31789e-12    -2.93676e-13    -2.40465e-1319  1.38537e-12 9.18101e-13 5.96147e-13 -2.41401e-12

And now write to_csv using the float_format='%.15f' parameter

df.to_csv('estc.csv',sep=',', float_format='%.15f') # write with precision .15

file output:

,0,1,2,30,-0.000000000002011,0.000000000001259,0.000000000001056,-0.0000000000005691,-0.000000000000687,0.000000000001919,0.000000000000526,0.0000000000003732,-0.000000000001481,0.000000000000063,-0.000000000001727,0.0000000000017293,-0.000000000000058,0.000000000000209,0.000000000000681,0.0000000000014904,-0.000000000000952,0.000000000000161,0.000000000000209,0.0000000000002115,-0.000000000000230,-0.000000000000141,0.000000000002326,-0.0000000000005096,0.000000000001162,0.000000000000618,0.000000000001632,0.0000000000015917,0.000000000000177,-0.000000000001659,0.000000000002187,-0.0000000000008458,0.000000000000766,0.000000000000129,-0.000000000000161,-0.0000000000003009,0.000000000000962,0.000000000000971,0.000000000000084,-0.00000000000064710,-0.000000000000628,-0.000000000001176,-0.000000000000360,0.00000000000086811,0.000000000000312,0.000000000000200,-0.000000000001107,-0.00000000000294512,-0.000000000000011,0.000000000000537,0.000000000001600,0.00000000000092013,-0.000000000000186,0.000000000001270,-0.000000000001048,-0.00000000000308314,0.000000000001674,0.000000000000074,0.000000000000328,0.00000000000005615,-0.000000000000532,-0.000000000000668,0.000000000000268,0.00000000000083716,-0.000000000000626,0.000000000000181,-0.000000000000268,0.00000000000115817,0.000000000000738,-0.000000000001765,-0.000000000000472,-0.00000000000030518,-0.000000000001061,-0.000000000001318,-0.000000000000294,-0.00000000000024019,0.000000000001385,0.000000000000918,0.000000000000596,-0.000000000002414

And now write to_csv using the float_format='%f' parameter

df.to_csv('estc.csv',sep=',', float_format='%f') # this will remove the extra zeros after the '.'

For more details check pandas.DataFrame.to_csv

Use the float_format argument:

In [11]: df = pd.DataFrame(np.random.randn(3, 3) * 10 ** 12)In [12]: dfOut[12]:              0             1             20  1.757189e+12 -1.083016e+12  5.812695e+111  7.889034e+11  5.984651e+11  2.138096e+112 -8.291878e+11  1.034696e+12  8.640301e+08In [13]: print(df.to_string(float_format='{:f}'.format))                     0                     1                   20 1757188536437.788086 -1083016404775.687134 581269533538.1702881  788903446803.216797   598465111695.240601 213809584103.1124572 -829187757358.493286  1034695767987.889160    864030095.691202

Which works similarly for to_csv:

df.to_csv('df.csv', float_format='{:f}'.format, encoding='utf-8')

If you would like to use the values as formated string in a list, say as part of csvfile csv.writier, the numbers can be formated before creating a list:

with open('results_actout_file','w',newline='') as csvfile:     resultwriter = csv.writer(csvfile, delimiter=',')     resultwriter.writerow(header_row_list)     resultwriter.writerow(df['label'].apply(lambda x: '%.17f' % x).values.tolist())